Forecasting multivariate time series is a core machine learning task both in science and in industry [1]}.
Between the individual time series (nodes), rich dependencies and interactions (edges) govern how the time series evolves.
In the simplest case these could be (linear) correlations; other examples include the road network underlying traffic flows [2]}, [3]}, or physical relations such as attraction or repulsion affecting trajectories of objects in space [4]}.
